class_docstr: A kind of Tensor that is to be considered a module parameter.
  
  Parameters are :class:`~torch.Tensor` subclasses, that have a
  very special property when used with :class:`Module` s - when they're
  assigned as Module attributes they are automatically added to the list of
  its parameters, and will appear e.g. in :meth:`~Module.parameters` iterator.
  Assigning a Tensor doesn't have such effect. This is because one might
  want to cache some temporary state, like last hidden state of the RNN, in
  the model. If there was no such class as :class:`Parameter`, these
  temporaries would get registered too.
  
  Args:
      data (Tensor): parameter tensor.
      requires_grad (bool, optional): if the parameter requires gradient. Note that
          the torch.no_grad() context does NOT affect the default behavior of
          Parameter creation--the Parameter will still have `requires_grad=True` in
          :class:`~no_grad` mode. See :ref:`locally-disable-grad-doc` for more
          details. Default: `True`
code: def __repr__(self):
      return "Parameter containing:\n" + super().__repr__()
  
imports: import torch
  from torch._C import _disabled_torch_function_impl
  from collections import OrderedDict
label_desc: Use image node_id 3 for calling the Parameter obj's underlying member method code with example usage: obj.__repr__() and returns: unknown